Overview

Dataset statistics

Number of variables18
Number of observations6368
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory316.7 B

Variable types

Text2
Numeric11
Categorical5

Alerts

Age is highly overall correlated with km_driven and 3 other fieldsHigh correlation
RPM is highly overall correlated with torque (Nm)High correlation
engine is highly overall correlated with log_sell and 4 other fieldsHigh correlation
fuel is highly overall correlated with torque (Nm)High correlation
km_driven is highly overall correlated with Age and 1 other fieldsHigh correlation
log_sell is highly overall correlated with Age and 5 other fieldsHigh correlation
max_power is highly overall correlated with engine and 4 other fieldsHigh correlation
seats is highly overall correlated with engineHigh correlation
selling_price is highly overall correlated with Age and 5 other fieldsHigh correlation
torque (Nm) is highly overall correlated with RPM and 5 other fieldsHigh correlation
transmission is highly overall correlated with max_powerHigh correlation
year is highly overall correlated with Age and 3 other fieldsHigh correlation
seller_type is highly imbalanced (67.5%)Imbalance
transmission is highly imbalanced (57.2%)Imbalance

Reproduction

Analysis started2023-11-23 07:57:07.194573
Analysis finished2023-11-23 07:57:24.493045
Duration17.3 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

name
Text

Distinct1820
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size561.2 KiB
2023-11-23T13:27:24.679353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length54
Median length42
Mean length25.251099
Min length11

Characters and Unicode

Total characters160799
Distinct characters68
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique798 ?
Unique (%)12.5%

Sample

1st rowMaruti Swift Dzire VDI
2nd rowSkoda Rapid 1.5 TDI Ambition
3rd rowHyundai i20 Sportz Diesel
4th rowHyundai Xcent 1.2 VTVT E Plus
5th rowMaruti 800 DX BSII
ValueCountFrequency (%)
maruti 2047
 
6.8%
hyundai 1117
 
3.7%
mahindra 692
 
2.3%
swift 606
 
2.0%
tata 562
 
1.9%
bsiv 536
 
1.8%
diesel 534
 
1.8%
1.2 480
 
1.6%
plus 473
 
1.6%
vxi 464
 
1.5%
Other values (768) 22591
75.0%
2023-11-23T13:27:25.079018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23735
 
14.8%
a 11752
 
7.3%
i 10755
 
6.7%
t 8160
 
5.1%
r 7188
 
4.5%
o 6376
 
4.0%
n 6070
 
3.8%
e 6001
 
3.7%
u 4791
 
3.0%
S 4220
 
2.6%
Other values (58) 71751
44.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82198
51.1%
Uppercase Letter 40970
25.5%
Space Separator 23735
 
14.8%
Decimal Number 10887
 
6.8%
Other Punctuation 2017
 
1.3%
Dash Punctuation 576
 
0.4%
Close Punctuation 208
 
0.1%
Open Punctuation 208
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11752
14.3%
i 10755
13.1%
t 8160
9.9%
r 7188
8.7%
o 6376
7.8%
n 6070
7.4%
e 6001
7.3%
u 4791
 
5.8%
d 3480
 
4.2%
l 3396
 
4.1%
Other values (16) 14229
17.3%
Uppercase Letter
ValueCountFrequency (%)
S 4220
 
10.3%
I 3763
 
9.2%
M 3606
 
8.8%
D 3331
 
8.1%
V 3257
 
7.9%
T 3034
 
7.4%
X 2623
 
6.4%
C 1972
 
4.8%
A 1759
 
4.3%
H 1691
 
4.1%
Other values (16) 11714
28.6%
Decimal Number
ValueCountFrequency (%)
1 2845
26.1%
0 2683
24.6%
2 2152
19.8%
5 833
 
7.7%
4 676
 
6.2%
8 578
 
5.3%
6 340
 
3.1%
3 275
 
2.5%
7 269
 
2.5%
9 236
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 2010
99.7%
/ 7
 
0.3%
Space Separator
ValueCountFrequency (%)
23735
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 576
100.0%
Close Punctuation
ValueCountFrequency (%)
) 208
100.0%
Open Punctuation
ValueCountFrequency (%)
( 208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 123168
76.6%
Common 37631
 
23.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11752
 
9.5%
i 10755
 
8.7%
t 8160
 
6.6%
r 7188
 
5.8%
o 6376
 
5.2%
n 6070
 
4.9%
e 6001
 
4.9%
u 4791
 
3.9%
S 4220
 
3.4%
I 3763
 
3.1%
Other values (42) 54092
43.9%
Common
ValueCountFrequency (%)
23735
63.1%
1 2845
 
7.6%
0 2683
 
7.1%
2 2152
 
5.7%
. 2010
 
5.3%
5 833
 
2.2%
4 676
 
1.8%
8 578
 
1.5%
- 576
 
1.5%
6 340
 
0.9%
Other values (6) 1203
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23735
 
14.8%
a 11752
 
7.3%
i 10755
 
6.7%
t 8160
 
5.1%
r 7188
 
4.5%
o 6376
 
4.0%
n 6070
 
3.8%
e 6001
 
3.7%
u 4791
 
3.0%
S 4220
 
2.6%
Other values (58) 71751
44.6%

year
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.9001
Minimum1994
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-11-23T13:27:25.440830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1994
5-th percentile2007
Q12012
median2014
Q32017
95-th percentile2019
Maximum2020
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.7372154
Coefficient of variation (CV)0.0018557104
Kurtosis1.8699148
Mean2013.9001
Median Absolute Deviation (MAD)3
Skewness-1.0704692
Sum12824516
Variance13.966779
MonotonicityNot monotonic
2023-11-23T13:27:25.592777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2017 800
12.6%
2016 689
10.8%
2015 677
10.6%
2018 605
9.5%
2014 576
9.0%
2012 562
8.8%
2013 554
8.7%
2011 504
7.9%
2019 346
5.4%
2010 321
5.0%
Other values (17) 734
11.5%
ValueCountFrequency (%)
1994 2
 
< 0.1%
1995 1
 
< 0.1%
1996 2
 
< 0.1%
1997 9
 
0.1%
1998 9
 
0.1%
1999 11
 
0.2%
2000 12
 
0.2%
2001 6
 
0.1%
2002 17
0.3%
2003 31
0.5%
ValueCountFrequency (%)
2020 62
 
1.0%
2019 346
5.4%
2018 605
9.5%
2017 800
12.6%
2016 689
10.8%
2015 677
10.6%
2014 576
9.0%
2013 554
8.7%
2012 562
8.8%
2011 504
7.9%

selling_price
Real number (ℝ)

HIGH CORRELATION 

Distinct655
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean541845.58
Minimum29999
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-11-23T13:27:25.759530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum29999
5-th percentile120000
Q1270000
median438999
Q3650000
95-th percentile1216500
Maximum10000000
Range9970001
Interquartile range (IQR)380000

Descriptive statistics

Standard deviation528647.17
Coefficient of variation (CV)0.97564175
Kurtosis52.431818
Mean541845.58
Median Absolute Deviation (MAD)188999
Skewness5.5908793
Sum3.4504727 × 109
Variance2.7946783 × 1011
MonotonicityNot monotonic
2023-11-23T13:27:25.929433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000 199
 
3.1%
350000 189
 
3.0%
600000 165
 
2.6%
400000 162
 
2.5%
550000 160
 
2.5%
500000 159
 
2.5%
450000 147
 
2.3%
250000 146
 
2.3%
650000 143
 
2.2%
700000 129
 
2.0%
Other values (645) 4769
74.9%
ValueCountFrequency (%)
29999 1
 
< 0.1%
30000 1
 
< 0.1%
31000 1
 
< 0.1%
31504 1
 
< 0.1%
33351 1
 
< 0.1%
35000 3
 
< 0.1%
39000 1
 
< 0.1%
40000 11
0.2%
42000 2
 
< 0.1%
45000 19
0.3%
ValueCountFrequency (%)
10000000 1
 
< 0.1%
7200000 1
 
< 0.1%
6523000 1
 
< 0.1%
6223000 1
 
< 0.1%
6000000 3
< 0.1%
5923000 1
 
< 0.1%
5850000 1
 
< 0.1%
5830000 1
 
< 0.1%
5800000 2
< 0.1%
5500000 4
0.1%

km_driven
Real number (ℝ)

HIGH CORRELATION 

Distinct869
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71829.13
Minimum1
Maximum2360457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-11-23T13:27:26.091814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10070
Q135000
median64650
Q3100000
95-th percentile152000
Maximum2360457
Range2360456
Interquartile range (IQR)65000

Descriptive statistics

Standard deviation58964.991
Coefficient of variation (CV)0.82090638
Kurtosis412.95674
Mean71829.13
Median Absolute Deviation (MAD)29650
Skewness12.401653
Sum4.574079 × 108
Variance3.4768702 × 109
MonotonicityNot monotonic
2023-11-23T13:27:26.256707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000 424
 
6.7%
70000 403
 
6.3%
80000 380
 
6.0%
60000 364
 
5.7%
50000 335
 
5.3%
90000 280
 
4.4%
100000 274
 
4.3%
40000 273
 
4.3%
110000 217
 
3.4%
30000 212
 
3.3%
Other values (859) 3206
50.3%
ValueCountFrequency (%)
1 1
 
< 0.1%
1000 5
0.1%
1300 1
 
< 0.1%
1303 1
 
< 0.1%
1500 2
 
< 0.1%
1600 1
 
< 0.1%
1620 1
 
< 0.1%
2000 7
0.1%
2118 1
 
< 0.1%
2136 1
 
< 0.1%
ValueCountFrequency (%)
2360457 1
< 0.1%
1500000 1
< 0.1%
577414 1
< 0.1%
500000 2
< 0.1%
475000 1
< 0.1%
440000 1
< 0.1%
426000 1
< 0.1%
380000 1
< 0.1%
376412 1
< 0.1%
375000 1
< 0.1%

fuel
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.4 KiB
Diesel
3493 
Petrol
2798 
CNG
 
50
LPG
 
27

Length

Max length6
Median length6
Mean length5.9637249
Min length3

Characters and Unicode

Total characters37977
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowDiesel
3rd rowDiesel
4th rowPetrol
5th rowPetrol

Common Values

ValueCountFrequency (%)
Diesel 3493
54.9%
Petrol 2798
43.9%
CNG 50
 
0.8%
LPG 27
 
0.4%

Length

2023-11-23T13:27:26.409987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-23T13:27:26.540431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
diesel 3493
54.9%
petrol 2798
43.9%
cng 50
 
0.8%
lpg 27
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 9784
25.8%
l 6291
16.6%
D 3493
 
9.2%
i 3493
 
9.2%
s 3493
 
9.2%
P 2825
 
7.4%
t 2798
 
7.4%
r 2798
 
7.4%
o 2798
 
7.4%
G 77
 
0.2%
Other values (3) 127
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31455
82.8%
Uppercase Letter 6522
 
17.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9784
31.1%
l 6291
20.0%
i 3493
 
11.1%
s 3493
 
11.1%
t 2798
 
8.9%
r 2798
 
8.9%
o 2798
 
8.9%
Uppercase Letter
ValueCountFrequency (%)
D 3493
53.6%
P 2825
43.3%
G 77
 
1.2%
C 50
 
0.8%
N 50
 
0.8%
L 27
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 37977
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9784
25.8%
l 6291
16.6%
D 3493
 
9.2%
i 3493
 
9.2%
s 3493
 
9.2%
P 2825
 
7.4%
t 2798
 
7.4%
r 2798
 
7.4%
o 2798
 
7.4%
G 77
 
0.2%
Other values (3) 127
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9784
25.8%
l 6291
16.6%
D 3493
 
9.2%
i 3493
 
9.2%
s 3493
 
9.2%
P 2825
 
7.4%
t 2798
 
7.4%
r 2798
 
7.4%
o 2798
 
7.4%
G 77
 
0.2%
Other values (3) 127
 
0.3%

seller_type
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
Individual
5690 
Dealer
651 
Trustmark Dealer
 
27

Length

Max length16
Median length10
Mean length9.6165201
Min length6

Characters and Unicode

Total characters61238
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual 5690
89.4%
Dealer 651
 
10.2%
Trustmark Dealer 27
 
0.4%

Length

2023-11-23T13:27:26.672134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-23T13:27:26.789837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
individual 5690
89.0%
dealer 678
 
10.6%
trustmark 27
 
0.4%

Most occurring characters

ValueCountFrequency (%)
d 11380
18.6%
i 11380
18.6%
a 6395
10.4%
l 6368
10.4%
u 5717
9.3%
I 5690
9.3%
v 5690
9.3%
n 5690
9.3%
e 1356
 
2.2%
r 732
 
1.2%
Other values (7) 840
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54816
89.5%
Uppercase Letter 6395
 
10.4%
Space Separator 27
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 11380
20.8%
i 11380
20.8%
a 6395
11.7%
l 6368
11.6%
u 5717
10.4%
v 5690
10.4%
n 5690
10.4%
e 1356
 
2.5%
r 732
 
1.3%
s 27
 
< 0.1%
Other values (3) 81
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
I 5690
89.0%
D 678
 
10.6%
T 27
 
0.4%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61211
> 99.9%
Common 27
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 11380
18.6%
i 11380
18.6%
a 6395
10.4%
l 6368
10.4%
u 5717
9.3%
I 5690
9.3%
v 5690
9.3%
n 5690
9.3%
e 1356
 
2.2%
r 732
 
1.2%
Other values (6) 813
 
1.3%
Common
ValueCountFrequency (%)
27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 11380
18.6%
i 11380
18.6%
a 6395
10.4%
l 6368
10.4%
u 5717
9.3%
I 5690
9.3%
v 5690
9.3%
n 5690
9.3%
e 1356
 
2.2%
r 732
 
1.2%
Other values (7) 840
 
1.4%

transmission
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.2 KiB
Manual
5811 
Automatic
 
557

Length

Max length9
Median length6
Mean length6.2624058
Min length6

Characters and Unicode

Total characters39879
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual 5811
91.3%
Automatic 557
 
8.7%

Length

2023-11-23T13:27:26.925250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-23T13:27:27.047130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
manual 5811
91.3%
automatic 557
 
8.7%

Most occurring characters

ValueCountFrequency (%)
a 12179
30.5%
u 6368
16.0%
M 5811
14.6%
n 5811
14.6%
l 5811
14.6%
t 1114
 
2.8%
A 557
 
1.4%
o 557
 
1.4%
m 557
 
1.4%
i 557
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33511
84.0%
Uppercase Letter 6368
 
16.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12179
36.3%
u 6368
19.0%
n 5811
17.3%
l 5811
17.3%
t 1114
 
3.3%
o 557
 
1.7%
m 557
 
1.7%
i 557
 
1.7%
c 557
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
M 5811
91.3%
A 557
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 39879
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12179
30.5%
u 6368
16.0%
M 5811
14.6%
n 5811
14.6%
l 5811
14.6%
t 1114
 
2.8%
A 557
 
1.4%
o 557
 
1.4%
m 557
 
1.4%
i 557
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12179
30.5%
u 6368
16.0%
M 5811
14.6%
n 5811
14.6%
l 5811
14.6%
t 1114
 
2.8%
A 557
 
1.4%
o 557
 
1.4%
m 557
 
1.4%
i 557
 
1.4%

owner
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
First Owner
4075 
Second Owner
1735 
Third Owner
420 
Fourth & Above Owner
 
133
Test Drive Car
 
5

Length

Max length20
Median length11
Mean length11.462783
Min length11

Characters and Unicode

Total characters72995
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst Owner
2nd rowSecond Owner
3rd rowFirst Owner
4th rowFirst Owner
5th rowSecond Owner

Common Values

ValueCountFrequency (%)
First Owner 4075
64.0%
Second Owner 1735
27.2%
Third Owner 420
 
6.6%
Fourth & Above Owner 133
 
2.1%
Test Drive Car 5
 
0.1%

Length

2023-11-23T13:27:27.169001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-23T13:27:27.293001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
owner 6363
48.9%
first 4075
31.3%
second 1735
 
13.3%
third 420
 
3.2%
fourth 133
 
1.0%
133
 
1.0%
above 133
 
1.0%
test 5
 
< 0.1%
drive 5
 
< 0.1%
car 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 11001
15.1%
e 8241
11.3%
n 8098
11.1%
6639
9.1%
O 6363
8.7%
w 6363
8.7%
i 4500
6.2%
t 4213
 
5.8%
F 4208
 
5.8%
s 4080
 
5.6%
Other values (14) 9289
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53349
73.1%
Uppercase Letter 12874
 
17.6%
Space Separator 6639
 
9.1%
Other Punctuation 133
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 11001
20.6%
e 8241
15.4%
n 8098
15.2%
w 6363
11.9%
i 4500
8.4%
t 4213
 
7.9%
s 4080
 
7.6%
d 2155
 
4.0%
o 2001
 
3.8%
c 1735
 
3.3%
Other values (5) 962
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
O 6363
49.4%
F 4208
32.7%
S 1735
 
13.5%
T 425
 
3.3%
A 133
 
1.0%
D 5
 
< 0.1%
C 5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
6639
100.0%
Other Punctuation
ValueCountFrequency (%)
& 133
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66223
90.7%
Common 6772
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 11001
16.6%
e 8241
12.4%
n 8098
12.2%
O 6363
9.6%
w 6363
9.6%
i 4500
6.8%
t 4213
 
6.4%
F 4208
 
6.4%
s 4080
 
6.2%
d 2155
 
3.3%
Other values (12) 7001
10.6%
Common
ValueCountFrequency (%)
6639
98.0%
& 133
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 11001
15.1%
e 8241
11.3%
n 8098
11.1%
6639
9.1%
O 6363
8.7%
w 6363
8.7%
i 4500
6.2%
t 4213
 
5.8%
F 4208
 
5.8%
s 4080
 
5.6%
Other values (14) 9289
12.7%

torque (Nm)
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.90744
Minimum14.9
Maximum3724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-11-23T13:27:27.441656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum14.9
5-th percentile62
Q1110
median170
Q3200
95-th percentile330
Maximum3724
Range3709.1
Interquartile range (IQR)90

Descriptive statistics

Standard deviation100.88477
Coefficient of variation (CV)0.5834611
Kurtosis251.73348
Mean172.90744
Median Absolute Deviation (MAD)56.225
Skewness8.5628549
Sum1101074.6
Variance10177.736
MonotonicityNot monotonic
2023-11-23T13:27:27.605565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 593
 
9.3%
190 548
 
8.6%
90 355
 
5.6%
114 227
 
3.6%
113 171
 
2.7%
62 161
 
2.5%
160 156
 
2.4%
69 131
 
2.1%
250 129
 
2.0%
330 121
 
1.9%
Other values (193) 3776
59.3%
ValueCountFrequency (%)
14.9 3
 
< 0.1%
24 10
 
0.2%
47.04 1
 
< 0.1%
48 6
 
0.1%
51 18
 
0.3%
57 2
 
< 0.1%
59 107
1.7%
59.78 11
 
0.2%
60 2
 
< 0.1%
62 161
2.5%
ValueCountFrequency (%)
3724 1
 
< 0.1%
1274 4
0.1%
1127 1
 
< 0.1%
789 3
< 0.1%
640 1
 
< 0.1%
620 6
0.1%
619 3
< 0.1%
600 3
< 0.1%
580 2
 
< 0.1%
560 2
 
< 0.1%

RPM
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3037.7164
Minimum400
Maximum5300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-11-23T13:27:27.772539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile1750
Q12250
median3000
Q34000
95-th percentile4500
Maximum5300
Range4900
Interquartile range (IQR)1750

Descriptive statistics

Standard deviation901.20446
Coefficient of variation (CV)0.29667169
Kurtosis-0.98698803
Mean3037.7164
Median Absolute Deviation (MAD)800
Skewness0.11390448
Sum19344178
Variance812169.49
MonotonicityNot monotonic
2023-11-23T13:27:27.929850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4000 840
13.2%
2000 738
11.6%
3500 668
10.5%
3000 555
 
8.7%
1750 509
 
8.0%
2500 483
 
7.6%
2750 398
 
6.2%
2800 371
 
5.8%
4200 208
 
3.3%
4500 192
 
3.0%
Other values (39) 1406
22.1%
ValueCountFrequency (%)
400 3
 
< 0.1%
480 1
 
< 0.1%
500 14
 
0.2%
1400 2
 
< 0.1%
1500 77
 
1.2%
1600 1
 
< 0.1%
1740 1
 
< 0.1%
1750 509
8.0%
1800 26
 
0.4%
1850 1
 
< 0.1%
ValueCountFrequency (%)
5300 1
 
< 0.1%
5200 1
 
< 0.1%
5000 26
 
0.4%
4850 23
 
0.4%
4800 80
1.3%
4750 8
 
0.1%
4700 8
 
0.1%
4600 58
 
0.9%
4500 192
3.0%
4400 63
 
1.0%

mileage
Real number (ℝ)

Distinct373
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.693841
Minimum9
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-11-23T13:27:28.088369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile12.99
Q117
median19.7
Q322.7
95-th percentile25.83
Maximum42
Range33
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation3.9388093
Coefficient of variation (CV)0.20000209
Kurtosis-0.29131507
Mean19.693841
Median Absolute Deviation (MAD)2.84
Skewness-0.0065653159
Sum125410.38
Variance15.514219
MonotonicityNot monotonic
2023-11-23T13:27:28.255331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.9 210
 
3.3%
19.7 167
 
2.6%
18.6 150
 
2.4%
21.1 147
 
2.3%
15.96 108
 
1.7%
17 91
 
1.4%
16.1 87
 
1.4%
15.1 86
 
1.4%
28.4 85
 
1.3%
12.8 85
 
1.3%
Other values (363) 5152
80.9%
ValueCountFrequency (%)
9 4
 
0.1%
9.5 1
 
< 0.1%
10 2
 
< 0.1%
10.1 2
 
< 0.1%
10.5 17
0.3%
10.75 2
 
< 0.1%
10.8 1
 
< 0.1%
10.9 2
 
< 0.1%
10.91 4
 
0.1%
10.93 3
 
< 0.1%
ValueCountFrequency (%)
42 1
 
< 0.1%
33.44 2
 
< 0.1%
33 1
 
< 0.1%
32.52 1
 
< 0.1%
32.26 1
 
< 0.1%
30.46 2
 
< 0.1%
28.4 85
1.3%
28.09 31
 
0.5%
27.62 6
 
0.1%
27.4 4
 
0.1%

engine
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1424.7778
Minimum624
Maximum3604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.6 KiB
2023-11-23T13:27:28.419420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum624
5-th percentile796
Q11196
median1248
Q31498
95-th percentile2499
Maximum3604
Range2980
Interquartile range (IQR)302

Descriptive statistics

Standard deviation496.68164
Coefficient of variation (CV)0.34860288
Kurtosis0.95731673
Mean1424.7778
Median Absolute Deviation (MAD)245
Skewness1.2229212
Sum9072985
Variance246692.65
MonotonicityNot monotonic
2023-11-23T13:27:28.582650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1248 907
 
14.2%
1197 662
 
10.4%
796 417
 
6.5%
998 398
 
6.2%
1498 336
 
5.3%
2179 336
 
5.3%
1396 263
 
4.1%
1199 192
 
3.0%
2523 181
 
2.8%
1198 163
 
2.6%
Other values (105) 2513
39.5%
ValueCountFrequency (%)
624 25
 
0.4%
793 6
 
0.1%
796 417
6.5%
799 71
 
1.1%
814 111
 
1.7%
909 2
 
< 0.1%
936 34
 
0.5%
993 26
 
0.4%
995 41
 
0.6%
998 398
6.2%
ValueCountFrequency (%)
3604 1
 
< 0.1%
3498 1
 
< 0.1%
3198 4
 
0.1%
2999 2
 
< 0.1%
2997 2
 
< 0.1%
2993 11
0.2%
2987 6
 
0.1%
2982 27
0.4%
2967 8
 
0.1%
2956 18
0.3%

max_power
Real number (ℝ)

HIGH CORRELATION 

Distinct299
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.858856
Minimum32.8
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-11-23T13:27:28.805103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum32.8
5-th percentile47.3
Q167.1
median81.86
Q3100
95-th percentile147.9
Maximum400
Range367.2
Interquartile range (IQR)32.9

Descriptive statistics

Standard deviation31.90044
Coefficient of variation (CV)0.36308736
Kurtosis5.4044068
Mean87.858856
Median Absolute Deviation (MAD)14.82
Skewness1.7004913
Sum559485.19
Variance1017.6381
MonotonicityNot monotonic
2023-11-23T13:27:28.975092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 324
 
5.1%
88.5 193
 
3.0%
46.3 158
 
2.5%
67 152
 
2.4%
67.1 141
 
2.2%
81.8 136
 
2.1%
67.04 136
 
2.1%
47.3 131
 
2.1%
62.1 128
 
2.0%
120 124
 
1.9%
Other values (289) 4745
74.5%
ValueCountFrequency (%)
32.8 2
 
< 0.1%
34.2 20
 
0.3%
35 18
 
0.3%
35.5 1
 
< 0.1%
37 88
1.4%
37.48 11
 
0.2%
37.5 5
 
0.1%
38 2
 
< 0.1%
38.4 2
 
< 0.1%
40.3 2
 
< 0.1%
ValueCountFrequency (%)
400 1
 
< 0.1%
282 1
 
< 0.1%
280 1
 
< 0.1%
272 1
 
< 0.1%
270.9 3
< 0.1%
265 1
 
< 0.1%
261.4 4
0.1%
258 2
< 0.1%
254.8 3
< 0.1%
254.79 1
 
< 0.1%

seats
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4356156
Minimum4
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-11-23T13:27:29.113127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum9
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.95932696
Coefficient of variation (CV)0.17648911
Kurtosis2.0184366
Mean5.4356156
Median Absolute Deviation (MAD)0
Skewness1.7257737
Sum34614
Variance0.92030821
MonotonicityNot monotonic
2023-11-23T13:27:29.235777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 4944
77.6%
7 958
 
15.0%
8 215
 
3.4%
4 124
 
1.9%
9 70
 
1.1%
6 57
 
0.9%
ValueCountFrequency (%)
4 124
 
1.9%
5 4944
77.6%
6 57
 
0.9%
7 958
 
15.0%
8 215
 
3.4%
9 70
 
1.1%
ValueCountFrequency (%)
9 70
 
1.1%
8 215
 
3.4%
7 958
 
15.0%
6 57
 
0.9%
5 4944
77.6%
4 124
 
1.9%

Brand
Categorical

Distinct31
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size443.3 KiB
Maruti
2047 
Hyundai
1117 
Mahindra
692 
Tata
562 
Honda
330 
Other values (26)
1620 

Length

Max length13
Median length10
Mean length6.276696
Min length2

Characters and Unicode

Total characters39970
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowMaruti
2nd rowSkoda
3rd rowHyundai
4th rowHyundai
5th rowMaruti

Common Values

ValueCountFrequency (%)
Maruti 2047
32.1%
Hyundai 1117
17.5%
Mahindra 692
 
10.9%
Tata 562
 
8.8%
Honda 330
 
5.2%
Ford 319
 
5.0%
Toyota 316
 
5.0%
Renault 206
 
3.2%
Chevrolet 197
 
3.1%
Volkswagen 166
 
2.6%
Other values (21) 416
 
6.5%

Length

2023-11-23T13:27:29.377463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maruti 2047
32.1%
hyundai 1117
17.5%
mahindra 692
 
10.9%
tata 562
 
8.8%
honda 330
 
5.2%
ford 319
 
5.0%
toyota 316
 
5.0%
renault 206
 
3.2%
chevrolet 197
 
3.1%
volkswagen 166
 
2.6%
Other values (21) 416
 
6.5%

Most occurring characters

ValueCountFrequency (%)
a 6954
17.4%
i 4021
10.1%
u 3482
8.7%
t 3430
8.6%
r 3310
8.3%
M 2829
 
7.1%
n 2682
 
6.7%
d 2598
 
6.5%
o 1740
 
4.4%
H 1447
 
3.6%
Other values (33) 7477
18.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33437
83.7%
Uppercase Letter 6494
 
16.2%
Dash Punctuation 39
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6954
20.8%
i 4021
12.0%
u 3482
10.4%
t 3430
10.3%
r 3310
9.9%
n 2682
 
8.0%
d 2598
 
7.8%
o 1740
 
5.2%
y 1433
 
4.3%
e 975
 
2.9%
Other values (13) 2812
8.4%
Uppercase Letter
ValueCountFrequency (%)
M 2829
43.6%
H 1447
22.3%
T 878
 
13.5%
F 362
 
5.6%
R 206
 
3.2%
C 197
 
3.0%
V 175
 
2.7%
B 81
 
1.2%
N 73
 
1.1%
S 63
 
1.0%
Other values (9) 183
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39931
99.9%
Common 39
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6954
17.4%
i 4021
10.1%
u 3482
8.7%
t 3430
8.6%
r 3310
8.3%
M 2829
 
7.1%
n 2682
 
6.7%
d 2598
 
6.5%
o 1740
 
4.4%
H 1447
 
3.6%
Other values (32) 7438
18.6%
Common
ValueCountFrequency (%)
- 39
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6954
17.4%
i 4021
10.1%
u 3482
8.7%
t 3430
8.6%
r 3310
8.3%
M 2829
 
7.1%
n 2682
 
6.7%
d 2598
 
6.5%
o 1740
 
4.4%
H 1447
 
3.6%
Other values (33) 7477
18.7%

Model
Text

Distinct1820
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size516.0 KiB
2023-11-23T13:27:29.634723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length45
Median length36
Mean length17.974246
Min length4

Characters and Unicode

Total characters114460
Distinct characters68
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique798 ?
Unique (%)12.5%

Sample

1st rowSwift Dzire VDI
2nd rowRapid 1.5 TDI Ambition
3rd rowi20 Sportz Diesel
4th rowXcent 1.2 VTVT E Plus
5th row800 DX BSII
ValueCountFrequency (%)
swift 606
 
2.6%
bsiv 536
 
2.3%
diesel 534
 
2.2%
1.2 480
 
2.0%
plus 473
 
2.0%
vxi 464
 
2.0%
vdi 443
 
1.9%
alto 394
 
1.7%
lxi 384
 
1.6%
dzire 349
 
1.5%
Other values (738) 19071
80.4%
2023-11-23T13:27:30.087151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17366
 
15.2%
i 6734
 
5.9%
e 5026
 
4.4%
a 4798
 
4.2%
t 4730
 
4.1%
o 4636
 
4.1%
S 4157
 
3.6%
r 3878
 
3.4%
I 3759
 
3.3%
n 3388
 
3.0%
Other values (58) 55988
48.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48761
42.6%
Uppercase Letter 34476
30.1%
Space Separator 17366
 
15.2%
Decimal Number 10887
 
9.5%
Other Punctuation 2017
 
1.8%
Dash Punctuation 537
 
0.5%
Open Punctuation 208
 
0.2%
Close Punctuation 208
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6734
13.8%
e 5026
10.3%
a 4798
9.8%
t 4730
9.7%
o 4636
9.5%
r 3878
8.0%
n 3388
 
6.9%
l 2817
 
5.8%
s 2089
 
4.3%
u 1309
 
2.7%
Other values (16) 9356
19.2%
Uppercase Letter
ValueCountFrequency (%)
S 4157
12.1%
I 3759
10.9%
D 3271
 
9.5%
V 3082
 
8.9%
X 2623
 
7.6%
T 2156
 
6.3%
C 1775
 
5.1%
A 1722
 
5.0%
B 1561
 
4.5%
L 1555
 
4.5%
Other values (16) 8815
25.6%
Decimal Number
ValueCountFrequency (%)
1 2845
26.1%
0 2683
24.6%
2 2152
19.8%
5 833
 
7.7%
4 676
 
6.2%
8 578
 
5.3%
6 340
 
3.1%
3 275
 
2.5%
7 269
 
2.5%
9 236
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 2010
99.7%
/ 7
 
0.3%
Space Separator
ValueCountFrequency (%)
17366
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 537
100.0%
Open Punctuation
ValueCountFrequency (%)
( 208
100.0%
Close Punctuation
ValueCountFrequency (%)
) 208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83237
72.7%
Common 31223
 
27.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6734
 
8.1%
e 5026
 
6.0%
a 4798
 
5.8%
t 4730
 
5.7%
o 4636
 
5.6%
S 4157
 
5.0%
r 3878
 
4.7%
I 3759
 
4.5%
n 3388
 
4.1%
D 3271
 
3.9%
Other values (42) 38860
46.7%
Common
ValueCountFrequency (%)
17366
55.6%
1 2845
 
9.1%
0 2683
 
8.6%
2 2152
 
6.9%
. 2010
 
6.4%
5 833
 
2.7%
4 676
 
2.2%
8 578
 
1.9%
- 537
 
1.7%
6 340
 
1.1%
Other values (6) 1203
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17366
 
15.2%
i 6734
 
5.9%
e 5026
 
4.4%
a 4798
 
4.2%
t 4730
 
4.1%
o 4636
 
4.1%
S 4157
 
3.6%
r 3878
 
3.4%
I 3759
 
3.3%
n 3388
 
3.0%
Other values (58) 55988
48.9%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0998744
Minimum3
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-11-23T13:27:30.242966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q16
median9
Q311
95-th percentile16
Maximum29
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.7372154
Coefficient of variation (CV)0.41068868
Kurtosis1.8699148
Mean9.0998744
Median Absolute Deviation (MAD)3
Skewness1.0704692
Sum57948
Variance13.966779
MonotonicityNot monotonic
2023-11-23T13:27:30.387561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
6 800
12.6%
7 689
10.8%
8 677
10.6%
5 605
9.5%
9 576
9.0%
11 562
8.8%
10 554
8.7%
12 504
7.9%
4 346
5.4%
13 321
5.0%
Other values (17) 734
11.5%
ValueCountFrequency (%)
3 62
 
1.0%
4 346
5.4%
5 605
9.5%
6 800
12.6%
7 689
10.8%
8 677
10.6%
9 576
9.0%
10 554
8.7%
11 562
8.8%
12 504
7.9%
ValueCountFrequency (%)
29 2
 
< 0.1%
28 1
 
< 0.1%
27 2
 
< 0.1%
26 9
 
0.1%
25 9
 
0.1%
24 11
 
0.2%
23 12
 
0.2%
22 6
 
0.1%
21 17
0.3%
20 31
0.5%

log_sell
Real number (ℝ)

HIGH CORRELATION 

Distinct655
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6158829
Minimum4.4771068
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-11-23T13:27:30.541609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4.4771068
5-th percentile5.0791812
Q15.4313638
median5.6424635
Q35.8129134
95-th percentile6.0851088
Maximum7
Range2.5228932
Interquartile range (IQR)0.38154959

Descriptive statistics

Standard deviation0.31865093
Coefficient of variation (CV)0.056741021
Kurtosis1.0260492
Mean5.6158829
Median Absolute Deviation (MAD)0.18684024
Skewness-0.17686852
Sum35761.942
Variance0.10153841
MonotonicityNot monotonic
2023-11-23T13:27:30.713401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.477121255 199
 
3.1%
5.544068044 189
 
3.0%
5.77815125 165
 
2.6%
5.602059991 162
 
2.5%
5.740362689 160
 
2.5%
5.698970004 159
 
2.5%
5.653212514 147
 
2.3%
5.397940009 146
 
2.3%
5.812913357 143
 
2.2%
5.84509804 129
 
2.0%
Other values (645) 4769
74.9%
ValueCountFrequency (%)
4.477106778 1
 
< 0.1%
4.477121255 1
 
< 0.1%
4.491361694 1
 
< 0.1%
4.498365699 1
 
< 0.1%
4.52310886 1
 
< 0.1%
4.544068044 3
 
< 0.1%
4.591064607 1
 
< 0.1%
4.602059991 11
0.2%
4.62324929 2
 
< 0.1%
4.653212514 19
0.3%
ValueCountFrequency (%)
7 1
 
< 0.1%
6.857332496 1
 
< 0.1%
6.814447379 1
 
< 0.1%
6.793999801 1
 
< 0.1%
6.77815125 3
< 0.1%
6.772541733 1
 
< 0.1%
6.767155866 1
 
< 0.1%
6.765668555 1
 
< 0.1%
6.763427994 2
< 0.1%
6.740362689 4
0.1%

Interactions

2023-11-23T13:27:22.747150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:09.897781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:11.136915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:12.380973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:13.780341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:15.053795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:16.265905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:17.514444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:18.988006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:20.301291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:21.515247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:22.860903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:10.017241image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:11.248334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:12.489013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:13.895378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:15.160802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:16.377556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:17.628994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:19.105000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:20.409785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:21.623006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:22.970913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:10.135589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:11.357896image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:12.598835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:14.013579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:15.272764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:16.491895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:17.744010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:19.231014image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:20.521789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:21.735745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:23.081385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:10.242740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:11.466403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:12.705178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:14.125583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:15.380425image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:16.601371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:17.849966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:19.346720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:20.629700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:21.843185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:23.198065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:10.356754image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:11.585404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:12.830148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:14.245579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:15.493429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:16.720394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:17.966969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:19.469242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:20.746861image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:21.957382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:23.305082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:10.459748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:11.694403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:12.933530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:14.352603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:15.592420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:16.826287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:18.074826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:19.579937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:20.850855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:22.063406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:23.417820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:10.574629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:11.808563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:13.184932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:14.470294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:15.702404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:16.938288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:18.193820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:19.698936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:20.960271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:22.174724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:23.553823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:10.686629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:11.923570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:13.326933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:14.586285image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:15.811728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:17.058297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:18.309831image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:19.815845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:21.075125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:22.287724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:23.693821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:10.806415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:12.048562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:13.453932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:14.709284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:15.927843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:17.180440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:18.652341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:19.936333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:21.192141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:22.417727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:23.804821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:10.915809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:12.158970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:13.563345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:14.825284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:16.048849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:17.293441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:18.765327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:20.050334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:21.298048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:22.528748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:23.912502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:11.026327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:12.268972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:13.670339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:14.935806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:16.157426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:17.403474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:18.877362image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:20.167371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:21.405248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T13:27:22.635726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-23T13:27:30.847981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
AgeBrandRPMenginefuelkm_drivenlog_sellmax_powermileageownerseatsseller_typeselling_pricetorque (Nm)transmissionyear
Age1.0000.198-0.1000.0160.1440.573-0.661-0.174-0.3140.256-0.0180.090-0.661-0.1130.171-1.000
Brand0.1981.000-0.092-0.0200.2640.084-0.048-0.0800.0190.1280.0640.184-0.048-0.0260.500-0.048
RPM-0.100-0.0921.000-0.3590.493-0.330-0.130-0.037-0.2050.066-0.1840.063-0.130-0.5090.1430.100
engine0.016-0.020-0.3591.0000.4690.3050.5280.741-0.4130.0750.5510.1070.5280.8510.398-0.016
fuel0.1440.2640.4930.4691.000-0.385-0.349-0.352-0.1140.025-0.3650.047-0.349-0.7660.0370.017
km_driven0.5730.084-0.3300.305-0.3851.000-0.2580.062-0.1770.0300.2170.000-0.2580.2490.018-0.573
log_sell-0.661-0.048-0.1300.528-0.349-0.2581.0000.658-0.0280.2750.3230.1781.0000.6290.4920.661
max_power-0.174-0.080-0.0370.741-0.3520.0620.6581.000-0.3200.0840.3320.1540.6580.7970.5110.174
mileage-0.3140.019-0.205-0.413-0.114-0.177-0.028-0.3201.0000.081-0.4610.031-0.028-0.1600.2420.314
owner0.2560.1280.0660.0750.0250.0300.2750.0840.0811.0000.0100.130-0.341-0.0290.106-0.464
seats-0.0180.064-0.1840.551-0.3650.2170.3230.332-0.4610.0101.0000.0290.3230.4510.0390.018
seller_type0.0900.1840.0630.1070.0470.0000.1780.1540.0310.1300.0291.000-0.191-0.1250.211-0.117
selling_price-0.661-0.048-0.1300.528-0.349-0.2581.0000.658-0.028-0.3410.323-0.1911.0000.6290.4630.661
torque (Nm)-0.113-0.026-0.5090.851-0.7660.2490.6290.797-0.160-0.0290.451-0.1250.6291.0000.3770.113
transmission0.1710.5000.1430.3980.0370.0180.4920.5110.2420.1060.0390.2110.4630.3771.000-0.161
year-1.000-0.0480.100-0.0160.017-0.5730.6610.1740.314-0.4640.018-0.1170.6610.113-0.1611.000

Missing values

2023-11-23T13:27:24.091022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-23T13:27:24.371952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

nameyearselling_pricekm_drivenfuelseller_typetransmissionownertorque (Nm)RPMmileageenginemax_powerseatsBrandModelAgelog_sell
0Maruti Swift Dzire VDI2014450000145500DieselIndividualManualFirst Owner190.002000.023.40124874.005.0MarutiSwift Dzire VDI95.653213
1Skoda Rapid 1.5 TDI Ambition2014370000120000DieselIndividualManualSecond Owner250.002500.021.141498103.525.0SkodaRapid 1.5 TDI Ambition95.568202
3Hyundai i20 Sportz Diesel2010225000127000DieselIndividualManualFirst Owner219.522750.023.00139690.005.0Hyundaii20 Sportz Diesel135.352183
5Hyundai Xcent 1.2 VTVT E Plus201744000045000PetrolIndividualManualFirst Owner113.754000.020.14119781.865.0HyundaiXcent 1.2 VTVT E Plus65.643453
7Maruti 800 DX BSII2001450005000PetrolIndividualManualSecond Owner59.002500.016.1079637.004.0Maruti800 DX BSII224.653213
8Toyota Etios VXD201135000090000DieselIndividualManualFirst Owner170.002400.023.59136467.105.0ToyotaEtios VXD125.544068
9Ford Figo Diesel Celebration Edition2013200000169000DieselIndividualManualFirst Owner160.002000.020.00139968.105.0FordFigo Diesel Celebration Edition105.301030
10Renault Duster 110PS Diesel RxL201450000068000DieselIndividualManualSecond Owner248.002250.019.011461108.455.0RenaultDuster 110PS Diesel RxL95.698970
11Maruti Zen LX200592000100000PetrolIndividualManualSecond Owner78.004500.017.3099360.005.0MarutiZen LX184.963788
12Maruti Swift Dzire VDi2009280000140000DieselIndividualManualSecond Owner190.002000.019.30124873.905.0MarutiSwift Dzire VDi145.447158
nameyearselling_pricekm_drivenfuelseller_typetransmissionownertorque (Nm)RPMmileageenginemax_powerseatsBrandModelAgelog_sell
8114Maruti Alto LXi201120000073000PetrolIndividualManualFirst Owner62.03000.019.7079646.305.0MarutiAlto LXi125.301030
8115Maruti 800 AC199740000120000PetrolIndividualManualFirst Owner59.02500.016.1079637.004.0Maruti800 AC264.602060
8116Maruti Alto K10 VXI Airbag201734000045000PetrolIndividualManualFirst Owner90.03500.023.9599867.105.0MarutiAlto K10 VXI Airbag65.531479
8118Hyundai i20 Magna201338000025000PetrolIndividualManualFirst Owner113.74000.018.50119782.855.0Hyundaii20 Magna105.579784
8119Maruti Wagon R LXI Optional201736000080000PetrolIndividualManualFirst Owner90.03500.020.5199867.045.0MarutiWagon R LXI Optional65.556303
8120Hyundai Santro Xing GLS2008120000191000PetrolIndividualManualFirst Owner96.13000.017.92108662.105.0HyundaiSantro Xing GLS155.079181
8121Maruti Wagon R VXI BS IV with ABS201326000050000PetrolIndividualManualSecond Owner90.03500.018.9099867.105.0MarutiWagon R VXI BS IV with ABS105.414973
8122Hyundai i20 Magna 1.4 CRDi201447500080000DieselIndividualManualSecond Owner219.72750.022.54139688.735.0Hyundaii20 Magna 1.4 CRDi95.676694
8123Hyundai i20 Magna2013320000110000PetrolIndividualManualFirst Owner113.74000.018.50119782.855.0Hyundaii20 Magna105.505150
8125Maruti Swift Dzire ZDi2009382000120000DieselIndividualManualFirst Owner190.02000.019.30124873.905.0MarutiSwift Dzire ZDi145.582063